Entropy distribution and condensation in random networks with a given degree distribution

Abstract

The entropy of network ensembles characterizes the amount of information encoded in the network structure, and can be used to quantify network complexity, and the relevance of given structural properties observed in real network datasets with respect to a random hypothesis. In many real networks the degrees of individual nodes are not fixed but change in time, while their statistical properties, such as the degree distribution, are preserved. Here we characterize the distribution of entropy of random networks with given degree sequences, where each degree sequence is drawn randomly from a given degree distribution. We show that the leading term of the entropy of scale-free network ensembles depends only on the network size and average degree, and that entropy is self-averaging, meaning that its relative variance vanishes in the thermodynamic limit. We also characterize large fluctuations of entropy that are fully determined by the average degree in the network. Finally, above a certain threshold, large fluctuations of the average degree in the ensemble can lead to condensation, meaning that a single node in a network of size~$N$ can attract $O(N)$ links.

(9 pages, 1 figure)

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• Sparse Maximum-Entropy Random Graphs with a Given Power-Law Degree Distribution
• Meta-validation of bipartite network projections
• Weighted hypersoft configuration model
• The role of adjacency matrix degeneration in maximum entropy weighted network models
• Statistical mechanics of random geometric graphs: Geometry-induced first order phase transition
• Entropy of labeled versus unlabeled networks
• Condensation of degrees emerging through a first-order phase transition in classical random graphs
• Hamiltonian Dynamics of Preferential Attachment
• Large deviation and anomalous fluctuations scaling in degree assortativity on configuration networks
• A Quantum-inspired Similarity Measure for the Analysis of Complete Weighted Graphs